Magdy M. El Rayes

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (Tm) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C =

Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS

8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Material Characterization of Power Plant Steel in the Virgin and Artificially-Aged Conditions

The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (), and material removal rate (MRR). The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected) as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations.

Microstructural and Mechanical Characterization of Artificially-Aged Power Plant Steel

In the present study the microstructural and mechanical properties including high-temperature tensile test of martensitic-ferritic steel 9–12% Cr were assessed. This steel, which is usually used in gas turbine power plants serving as shroud, was tested in the as-received (virgin) and artificially-aged condition for 1344hrs (8 weeks) at 700 °C and the results were compared and analyzed. The high-temperature service of this steel suggested investigating the high temperature tensile behavior and the deformation mechanism occurring at different testing temperature and strain rates which were 540, 580 and 620 °C and 10−3, 10−4 and 10−5 s−1, respectively. The results showed that the microstructural features such as phases and carbides are factors which influence the high temperature mechanical properties. The stress- strain rate curves showed a large stress exponent of ≈ 15, indicating that the materials behavior lie in the power law breakdown regime. Based on stress vs. the reciprocal of temperature, the apparent activation energy was calculated as 443 kJ/ Mole. Plotting Zener Holloman parameter versus true stress made it possible to mathematically model all test results into a unified model.

Transition from Superplastic Behavior - Viscous Glide - Dislocation Climb - Power-Law Break down Regimes in Friction Stir Processed AA5083

Degradation of properties due to ageing at high temperature service is a common problem which normally occurs in electricity generation power plants, refineries, petrochemical industries. This work investigates the influence of accelerated artificial ageing on the microstructural and mechanical properties of 9-12%Cr steel usually used in power plants. This steel was tested in the as-produced and aged for 2, 5 and 8 weeks at 700°C. Results showed that the microstructural features such as phases, carbides, i.e. morphology and type, sub-grains and their boundaries and misorientation angles are the dominant factors influencing the mechanical properties. Ageing led carbides to go into solution in the martensite-ferrite structure and also led to the increase of average misorientation angle as well as to the reduction of low angle grain boundaries. In addition, ageing led to the reduction in the mechanical properties and hardness when compared to the as-produced condition.

Microstructural and Mechanical Characterization of Friction Stir Welded- 1050 Aluminium Alloy

5083 Al alloy was friction stir processed (FSP) at room temperature under various experimental conditions. Two rotational speeds of 430 and 850 rpm with a single traverse feed of 90 mm/min (430-90, 850-90) were used, to investigate the effect of rotation speed. Also, another feed rate of 140 mm/min was used with a rotational speed of 430 rpm (430-140), to investigate the effect of feed rate. The processing conditions resulted in three different grain sizes of 0.95, 1.6 and 2.6 μm depending on the FSP parameters. The deformation behavior of the FSP samples was investigated at 250 C at three strain rates of 10-4, 10-3 and 10-2 s-1. The values of strain rate sensitivity, m was determined, as a function of grain size, and it decreased from 0.45 to 0.33 to 0.18 with increasing the grain size. True activation energy was calculated as 63, 95, 157 kJ/mole for the grain sizes of 0.95, 1.6 and 2.6 μm, respectively. These calculated values are comparable to grain boundary sliding of magnesium in aluminum (69 – 78 kJ mol-1), magnesium in aluminum (~115 kJ mole-1) and aluminum lattice diffusion (~143 kJ mol-1 ). The change in the deformation mechanism with grain size was discussed in some details.

Erosion - Corrosion of Cermet Coating

Friction stir welding (FSW) is a fairly recent technique that utilizes a non-consumable rotating welding tool to generate frictional heat and plastic deformation at the welding location in the continuously-fed work piece. In the present investigation this welding process is applied to join 1050 cold-rolled aluminium plates. The effects of varying the welding parameters namely welding speed [56, 90 and 140 mm/min] and tool rotational speed [850 and 1070 rpm] on the mechanical and microstructural properties were studied. Vickers micro hardness results across the weldment showed that the weld nugget hardness is dependant upon the welding speed and the tool rotational speed. Increasing the welding speed at 850 rpm reduced the hardness at the weld nugget, whereas, at 1070 rpm the weld nugget hardness merely did not change. However, the hardness achieved at 850 rpm was constantly higher than that achieved with 1070 rpm irrespective to welding speeds. In the same fashion, the yield and ultimate strengths of the joints were influenced by varying the welding parameters. Increasing the welding speed at 850 rpm reduced both strengths whereas; at 1070 rpm they were almost unchanged. Microstructural study showed that the weld region is composed of unaffected base metal and the stir zoned [weld nugget] which is characterised by a fine equiaxed grain structure. Increasing the welding speed at constant tool rotational speed has caused a slight refinement in the weld nuggets grain size, whereas, decreasing the rotational speed has also led to weld nugget grain refinement.